Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/110853
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dc.contributor.advisor徐士勛zh_TW
dc.contributor.author王姸之zh_TW
dc.creator王姸之zh_TW
dc.date2017en_US
dc.date.accessioned2017-07-11T04:06:48Z-
dc.date.available2017-07-11T04:06:48Z-
dc.date.issued2017-07-11T04:06:48Z-
dc.identifierG0104258028en_US
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/110853-
dc.description碩士zh_TW
dc.description國立政治大學zh_TW
dc.description經濟學系zh_TW
dc.description104258028zh_TW
dc.description.abstract本研究主要針對 Breidt et al.(1991) 等多位學者所建構的 Mixed causal-noncausal model,探討其假設與可拆解特性,並仔細討論相關資料模擬估計及預測的方法,最後將其實際應用於隱含波動率指數 (Volatility Index)的估計及預測上。根據本研究的實證結果,我們發現隱含波動率指數確實包含非因果的特性,並可進一步對其拆解及預測。另外 , 我們也以移動窗格的方式觀察係數估計結果的變化,發現 Mixed Causal-Noncausal Model 的確能夠捕捉到泡沫或危機正在生成的過程。zh_TW
dc.description.abstractThis paper first focuses on Mixed causal-noncausal model constructed by Breidt et al.(1991) and then conducts empirical research on the CBOE Volatility Index. The assumptions, simulation, estimation and prediction methods of Mixed causal-noncausal model are introduced in great detail. Our empirical results show that the CBOE Volatility Index really contains non-causal parts, such that we can filter this part from the index and then further predict it. Moreover, by employing the rolling window estimation scheme the resulting coefficients of Mixed causal-noncausal model really could detect a bubble or a crisis which is going to happen.en_US
dc.description.tableofcontents1 緒論 1\n2 文獻回顧 4\n2.1 計量模型的相關文獻探討 4\n2.2 隱含波動率指數的相關文獻探討 6\n3 實證模型 8\n3.1 Mixed causal-noncausal model 介紹 8\n3.2 Mixed causal-noncausal model 的拆解 10\n3.3 Mixed causal-noncausal model 的模擬 10\n3.4 Mixed causal-noncausal model 的估計 13\n3.5 Mixed causal-noncausal model 的預測 14\n4 實證方法 17\n4.1 資料說明 17\n4.2 資料敘述統計 18\n4.3 研究方法 19\n5 實證結果 21\n5.1 ADF單根檢定 21\n5.2 選取期數p 21\n5.3 選取最適模型 22\n5.4 資料的拆解 26\n5.5 資料的預測 28\n5.6 不同資料期間的估計結果 30\n6 結論 34zh_TW
dc.format.extent2295052 bytes-
dc.format.mimetypeapplication/pdf-
dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G0104258028en_US
dc.subject非因果模型zh_TW
dc.subject混合模型zh_TW
dc.subject隱含波動率指數zh_TW
dc.subject可拆解性質zh_TW
dc.subjectNoncausalen_US
dc.subjectMixed causal-noncausal modelen_US
dc.subjectVIXen_US
dc.subjectFilteren_US
dc.title隱含波動率指數的分析及預測 - Mixed Causal-Noncausal Model 的應用zh_TW
dc.titleModeling and Predicting The CBOE Volatility Index - Application of Mixed Causal-Noncausal Modelen_US
dc.typethesisen_US
dc.relation.reference王維安 (2010), “VIX 指數之 Levy 模型最適化估計與預測及 VIX 衍生性商品之定價” 國立高雄應用科技大學金融資訊研究所學位論文。\n佟劭文 (2014), “以 VIX 指數作為擇時指標-探討七大工業國股票市場” 義守大學財務金融學系學位論文。\n周聖淵 (2012), “恐慌指數交易策略在股市之實證研究” 暨南大學經營管理碩士在職專班學位論文。\n陳志杰 (2012), “台灣大型權值股股價報酬與 VIX 指數, 黃金報酬之關聯性分析” 臺北大學國際財務金融碩士在職專班學位論文。\n張永杰 (2015), “VIX 指數, S&P500 指數與黃金價格之關聯性研究” 臺北大學國際財務金融碩士在職專班學位論文。\n黃冠甄 (2016), “VIX 指數, 美元指數及石油期貨價格對黃豆期貨價格及對咖啡期貨價格之影響” 中原大學企業管理研究所學位論文。\nAhoniemi, K. (2008). Modeling and forecasting the VIX index. Working paper.\nAndrews, B., Calder, M., and Davis, R. A. (2009). Maximum likelihood estimation for α -stable autoregressive processes. The Annals of Statis-tics, 37(4), 1946–1982.\nAndrews, B., and Davis, R. A. (2013). Model identification for infinite variance autoregressive processes. Journal of Econometrics, 172(2), 222–234.\nBerger, J. M., and Mandelbrot, B. (1963). A new model for error clustering in telephone circuits. IBM Journal of Research and Development, 7(3), 224–236.\nBreidt, F. J., Davis, R. A., Lh, K. S., and Rosenblatt, M. (1991). Maximum likelihood estimation for noncausal autoregressive processes. Journal of Multivariate Analysis, 36(2), 175–198.\nBreidt, F. J., and Davis, R. A. (1992). Time-reversibility, identifiability and independence of innovations for stationary time series. Journal of Time Series Analysis, 13(5), 377–390.\nBrockwell, P. J., and Davis, R. A. (1991). Time series: theory and methods.Springer. New York.\nDickey, D. A., and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427–431.\nFama, E. F. (1965). The behavior of stock-market prices. The journal of Business, 38(1), 34–105.\nFernandes, M., Medeiros, M. C., and Scharth, M. (2014). Modeling and predicting the CBOE market volatility index. Journal of Banking and Finance, 40, 1–10.\nGourieroux, C., and Zakoian, J. M. (2013). Explosive bubble modelling by noncausal process. Working paper.\nGourieroux, C., and Jasiak, J. (2015). Filtering, prediction and simulation methods for noncausal processes. Journal of Time Series Analysis, 37, 405–430.\nGourieroux, C., and Zakoian, J. M. (2017). Local explosion modelling by non-causal process. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(3), 737–756.\nHecq, A., Lieb, L., and Telg, S. (2015a). Forecasting inflation in Europe with Mixed Causal-Noncausal Models. Working paper. \nHecq, A., Lieb, L., and Telg, S. (2015b). Identification of Mixed Causal-Noncausal Models: How fat should we go? Working paper.\nHencic, A., and Gourieroux, C. (2015). Noncausal autoregressive model in application to Bitcoin/USD exchange rates. Econometrics of Risk, 583, 17–40.\nLanne, M., Luoto, J., and Saikkonen, P. (2012). Optimal forecasting of noncausal autoregressive time series. International Journal of Fore-casting, 28(3), 623–631.\nLanne, M. and Saikkonen, P. (2011). Noncausal autoregressions for economic time series. Journal of Time Series Econometrics, 3(3), Article 2.\nSaid, S. E., and Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607.zh_TW
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